Body impedance analysis (BIA) is used to evaluate the human body composition by measuring the resistance and reactance of human tissues with a high-frequency, low-intensity electric current. Nonetheless, the estimation of the body composition is influenced by many factors: body status, environmental conditions, instrumentation, and measurement procedure. This work studies the effect of the connection cables, conductive electrodes, adhesive gel, and BIA device characteristics on the measurement uncertainty. Tests were initially performed on electric circuits with passive elements and on a jelly phantom simulating the body characteristics. Results showed that the cables mainly contribute to increase the error on the resistance measurement, while the electrodes and the adhesive introduce a negligible disturbance on the measurement chain. This paper also proposes a calibration procedure based on a multivariate linear regression to compensate for the systematic error effect of BIA devices.

Metrological characterization of instruments for body impedance analysis

Marcotuli, Valerio;Moorhead, Alex P.;Tarabini, Marco
2022-01-01

Abstract

Body impedance analysis (BIA) is used to evaluate the human body composition by measuring the resistance and reactance of human tissues with a high-frequency, low-intensity electric current. Nonetheless, the estimation of the body composition is influenced by many factors: body status, environmental conditions, instrumentation, and measurement procedure. This work studies the effect of the connection cables, conductive electrodes, adhesive gel, and BIA device characteristics on the measurement uncertainty. Tests were initially performed on electric circuits with passive elements and on a jelly phantom simulating the body characteristics. Results showed that the cables mainly contribute to increase the error on the resistance measurement, while the electrodes and the adhesive introduce a negligible disturbance on the measurement chain. This paper also proposes a calibration procedure based on a multivariate linear regression to compensate for the systematic error effect of BIA devices.
2022
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1223937
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